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Experimental Exploration of Compact Convolutional Neural Network Architectures for Non-temporal Real-time Fire Detection - supporting materials

Pretrained Neural Network Models for Samarth 2019 study - TensorFlow format Open Access

In this work we explore different Convolutional Neural Network (CNN) architectures and their variants for non-temporal binary fire detection and localization in video or still imagery. We consider the performance of experimentally defined, reduced complexity deep CNN architectures for this task and evaluate the effects of different optimization and normalization techniques applied to different CNN architectures (spanning the Inception, ResNet and EfficientNet architectural concepts). Contrary to contemporary trends in the field, our work illustrates a maximum overall accuracy of 0.96 for full frame binary fire detection and 0.94 for superpixel localization using an experimentally defined reduced CNN architecture based on the concept of InceptionV4. We notably achieve a lower false positive rate of 0.06 compared to prior work in the field presenting an efficient, robust and real-time solution for fire region detection. | Cited in: Experimental Exploration of Compact Convolutional Neural Network Architectures for Non-temporal Real-time Fire Detection (G. Samarth, N. Bhowmik, T.P. Breckon), In Proc. Int. Conf. on Machine Learning Applications, IEEE, 2019. | This file contains supporting materials in the form of the pre-trained network models used in the study.

Descriptions

Resource type
Other
Contributors
Contact person: Breckon, Toby 1
Creator: Samarth, Ganesh 2
Editor: Bhowmik, Neelanjan 1
1 Durham University, UK
2 Institute of Technology Dharwad, India
Funder
Durham University, UK
Research methods
Other description
Keyword
Convolutional Neural Network
fire detection
Subject
Computer Science
Engineering
Location
Durham, UK
Language
English
Cited in
Experimental Exploration of Compact Convolutional Neural Network Architectures for Non-temporal Real-time Fire Detection (G. Samarth, N. Bhowmik, T.P. Breckon), In Proc. Int. Conf. on Machine Learning Applications, IEEE, 2019.
Identifier
ark:/32150/r25x21tf409
doi:10.15128/r25x21tf409
Rights
MIT Licence (MIT)

Creative Commons Attribution 4.0 International (CC BY)

Publisher
Durham University
Date Created
September 2019

File Details

Depositor
T. Breckon
Date Uploaded
Date Modified
11 December 2019, 12:12:24
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Characterization
File format: zip (ZIP Format)
Mime type: application/zip
File size: 158802570
Last modified: 2019:12:11 09:24:44+00:00
Filename: samarth-2019-fire-detection-pretrained-models.zip
Original checksum: efa859a317ea0cb2ac27834662137500
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User N. Syrotiuk has updated Pretrained Neural Network Models for Samarth 2019 study - TensorFlow format about 5 years ago
User N. Syrotiuk has updated Pretrained Neural Network Models for Samarth 2019 study - TensorFlow format about 5 years ago
User N. Syrotiuk has updated Pretrained Neural Network Models for Samarth 2019 study - TensorFlow format about 5 years ago
User N. Syrotiuk has updated Pretrained Neural Network Models for Samarth 2019 study - TensorFlow format about 5 years ago
User N. Syrotiuk has updated Pretrained Neural Network Models for Samarth 2019 study - TensorFlow format about 5 years ago
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User T. Breckon has deposited samarth-2019-fire-detection-pretrained-models.zip about 5 years ago